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Effect of Trade Liberalization on Gender Inequality: The Case of India

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Abstract

Using a panel of establishments from the annual survey of industries, I study the impact of the 1991 trade liberalization episode in India on the employment share of women. Contrary to the predictions of a taste-based discrimination model, I find that establishments exposed to larger output tariff reductions and import competition reduced the share of female workers. I also find that input tariff reductions neither raised nor reduced female employment share. The negative association between output tariff reductions and female employment appears to be driven by establishments which increased the number of shifts per worker. Since women in India are prohibited by law from working long hours and night shifts, this hours-constraint appears to have reduced relative employment of women. This paper is the first to provide empirical evidence of how an ostensibly pro-women policy of limiting female work hours might have unintended side effects. In order to look at the overall effects of liberalization on the gender employment share, I use Census of India data to create a district level panel. I find that districts which were more exposed to the reforms experienced a reduction in the share of female workers. This was observed for both urban and rural areas.

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Notes

  1. Dix-Carnerio and Kovak (2015), Revenga (1997), Hanson and Harrison (1999), Feliciano (2001), Currie and Harrison (1997) show evidence of this from a wide range of countries such as Brazil, Mexico, Morocco, Chile, Argentina, and Columbia.

  2. I include only large establishments in the establishment-level panel as these are the census of all establishments in that category. I aggregate both the large and small establishments at the industry level and construct an industry level panel for the formal sector manufacturing establishments. See section Data for more details.

  3. In the context of this study, I find that the output tariff change is also uncorrelated with the log female to male share in man-days in 1989, the log skill ratio in 1989 and log male intensity in 1989 at the industry level (the results are presented in Table 12).

  4. See Data Section below for more details.

  5. All establishments with 100 workers or above were surveyed in 1989, and all establishments with 200 workers or above were surveyed in 1998.

  6. I aggregate up to 3-digit industry level. There are 90 industries.

  7. I also look at the panel of smaller establishments. I do not include the results for smaller establishments in the paper since, in the ASI data, only a sample of the small establishments were reported in both years. Also, the sampling technique changed between 1989 and 1998. Earlier, 1/3rd of the firms were sampled, which changed to 1/7th. So if a small establishment was not part of the panel, one is not sure if it did not exist that year or was left out sampled because of the sampling scheme. The ASI provides multipliers for sampled establishments, which is a measure of the probability of being sampled. These were used to calculate the aggregates for the industry level.

  8. The more commonly used National Sample Survey data is not representative at the district level for urban areas (see Topalova 2010; Edmonds et al. 2010). Hence, I prefer to use Census data in this paper to construct the district level panel.

  9. see Appendix Table 13.

  10. In my analysis, I do not look at the change in relative wages, as I do not have information for wages for the “pre” period in the ASI. Figure 3 shows that the correlation between log ratio of female to total share in man-days and log ratio of female to total share in the wage bill for 1998 is 0.95.

  11. The change in tariffs is in percentage points.

  12. I am grateful to Reshad Ahsan and Debashish Mitra for sharing their tariff data.

  13. These classifications were revised between 1989 and 1998. I converted all industry classifications to the 1998 NIC codes using concordance tables provided by Ministry of Statistics and Program Implementation (MOSPI).

  14. Input tariffs were constructed by Ahsan and Mitra (2014) using the formula used by Amiti et al. (2012). Consider industry j that uses inputs from industry k. In this case \(\text{ Input } \text{ Tariff}_{jt}=\sum _{k}s_{jk}*\text{ Output } \text{ Tariff}_{kt}\), where \( s_{jk}\) is the share of input k used in producing output j. The share of inputs are obtained from the relevant input–output tables.

  15. For convenience, however, I will use the terms “firm” and “establishment” interchangeably in the paper.

  16. These are registered under the Factories Act of 1948. This includes all establishments using 10 or more workers if using power and 20 or more workers if not using power.

  17. I use the detailed unit level data from the annual survey of industries. This is the most detailed version of the ASI data which gives the breakup of hours and employment by establishments for production workers. The ASI provides data at a greater level of aggregation such as summary data and industry level , all of which are used by some recent papers such as Banerjee and Veeramani (2017), Adhvaryu et al. (2013) and Nataraj (2011).

  18. All establishments with 100 workers or above were surveyed in 1989 and all establishments with 200 workers or above were surveyed in 1998.

  19. The sample scheme surveyed approximately one third of the establishments below the size cut-off every year, subject to the constraint that a sufficient number of establishments were sampled to assure representativeness at the state and industry level.

  20. The match rate and summary statistics are reported in Tables 1 and 14.

  21. Around 43.40% of the big establishments in 1989 are matched and included in the panel data set. This is expected, given that Hsieh and Klenow (2014) find that the exit rate of large establishments is around 4% every year. The match rate among smaller establishments is even lower. Around 7% of the smaller establishments in the 1989 sample are matched and included in the panel data set.

  22. Following the size cut-offs for being in the census of establishments, I classify establishments with > 60,000 man-days as “big” establishments. This definition of the census sector is taken according to 1998. However, only 5% of these establishments were not part of the census sector in 1989. Even if I drop these 5% establishments, the results remain similar. Also, I do not find any correlation between change in tariff and the total size of establishments.

  23. The summary tables for the cross section are given in Table 15. The share of female to total man-days and numbers are slightly lesser in the panel establishments than in the cross section. In my analysis, I look at the percentage change in female shares in response to change in tariffs. When I include all the establishments and aggregate up to the industry, the direction of change is similar and statistically significant.

  24. The 1991 census data was used by Topalova (2010) to construct the district level tariff intensity measures.

  25. This is similar to usual activity status in the NSS data.

  26. NIC 1987 codes 200 to 400.

  27. I also look at the change in ratio of female to total man-days (in levels) and find no difference in results.

  28. Years for outcome variables are 1991 and 2001. Using lagged tariffs should not make a lot of difference to the analysis as most of the tariff changes occurred between 1989 and 1997 (Fig. 5).

  29. I also use the same specification as Topalova (2010) and the results remain similar.

  30. I cluster standard errors at the 3-digit industry level.

  31. I have also interacted the importer dummy with input tariff changes to check for differential effects and found none of the interactions to be statistically significant.

  32. Figure 4 shows that the correlation between output and input tariff is 0.61.

  33. The IT sector is not subject to these restrictions. Begum (2013) studies the effect of night shifts on the health of women in the IT sector. Recent newspaper articles reported that states are actively considering repealing this section of the Act. The state governments have been given authority to make amendments to the law.

  34. see Hasan et al. (2007), Ahsan et al. (2012), Ahsan (2013) which look at demand elasticity, unemployment and productivity as some outcomes.

  35. The years are 2000 to 2007 which is much after our reference period. I assume here that the enforcement intensity in states have not undergone major changes.

  36. In Table 17 columns (4) and (5), I look at the change in log ratio of plant and machinery to man-days and the change in log ratio of fixed capital to sales as alternative measures of skill upgrading. However, I do not observe evidence of skill upgrading with respect to output tariff changes.

  37. I take controls for import status of the establishment in the initial year. As mentioned earlier, I do not have information on the export status of establishments. In order to take care of this issue, I take the share of fixed capital to sales and working capital to sales and use them as a proxy for exports.

  38. Equations (1) and (2).

  39. In column (3), I look at the change in log ratio of total sales to man-days and find that there is a decline overall with respect to a decline in output as well as input tariffs.

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Acknowledgements

This work was done as part of my Ph.D. dissertation at the university of Houston. I am thankful to Chinhui Juhn, Aimee Chin and Elaine Liu for their useful comments and suggestions. I am also thankful to the faculty and my fellow graduate students at the university of Houston for useful discussions. I thank university of Houston for the research support. I also thank the faculty and students at Wageningen University for various useful comments. I thank Janneke Pieters, Reshad Ahsan, Sourav Chakraborty, Allan Collard-Wexler, Siddharth Kothari and Shaibal Gupta for providing very insightful suggestions and support.

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    Ashmita Gupta

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Appendix

Appendix

See Figs. 1, 2, 3, 4, 5 and Tables 12, 13, 14, 15, 16, 17, 18, 19, 20, 21.

Fig. 1
figure 1

Change in output tariff and initial output tariff in 1988

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Fig. 2
figure 2

Change in input tariff and initial input tariff in 1988

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Fig. 3
figure 3

Relationship between log Wage Bill share and log man-day share in 1998

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Fig. 4
figure 4

Relationship of output tariff and input tariff change

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Fig. 5
figure 5

Change in average output tariff

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Table 12 Exogeneity of tariff change
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Table 13 Education among males and females in manufacturing industries
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Table 14 Panel match rate table
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Table 15 Summary statistics-big and private cross section
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Table 16 Share of female share and tariffs including other reform controls
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Table 17 Tariffs and other establishment level outcomes
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Table 18 10 Industries with the highest female share
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Table 19 10 Industries with lowest female share
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Table 20 Effect of female share on output tariff by initial male intensity distribution and initial female share as a control
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Table 21 Robustness with additional controls
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Gupta, A. Effect of Trade Liberalization on Gender Inequality: The Case of India. IMF Econ Rev 69, 682–720 (2021). https://doi.org/10.1057/s41308-021-00143-7

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